#!/bin/bash

################基础配置参数，需要模型审视修改##################
# 必选字段(必须在此处定义的参数): Network batch_size 
# 网络名称，同目录名称
Network="Swin-Transformer"
# 训练batch_size
batch_size=256
device_number=8
iter=110
export NPROC=${device_number}
# 参数校验，data_path为必传参数，其他参数的增删由模型自身决定；此处新增参数需在上面有定义并赋值
for para in $*
do
    if [[ $para == --workers* ]];then
        workers=`echo ${para#*=}`
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
   elif [[ $para == --node_rank* ]];then
        node_rank=`echo ${para#*=}`
   elif [[ $para == --master_addr* ]];then
        master_addr=`echo ${para#*=}`
   elif [[ $para == --data_shuffle* ]];then
        data_shuffle=`echo ${para#*=}`
   elif [[ $para == --iter* ]];then
        iter=`echo ${para#*=}`
   elif [[ $para == --master_port* ]];then
        master_port=`echo ${para#*=}`
   elif [[ $para == --nnodes* ]];then
        nnodes=`echo ${para#*=}`
    fi
done


export WORLD_SIZE=$((nnodes * device_number))
export HCCL_WHITELIST_DISABLE=1
export HCCL_IF_IP=$(hostname -I |awk '{print $1}')

# 校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi


###############指定训练脚本执行路径###############
# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
cur_path=`pwd`
cur_path_last_diename=${cur_path##*/}
if [ x"${cur_path_last_diename}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi


#################创建日志输出目录，不需要修改#################
ASCEND_DEVICE_ID=0
if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
    rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
else
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
fi


#################启动训练脚本#################
#训练开始时间，不需要修改
start_time=$(date +%s)
# 非平台场景时source 环境变量
check_etp_flag=`env | grep etp_running_flag`
etp_flag=`echo ${check_etp_flag#*=}`
if [ x"${etp_flag}" != x"true" ];then
    source ${test_path_dir}/env_npu.sh
fi

RANK_ID_START=0

for((RANK_ID=$RANK_ID_START;RANK_ID<$((NPROC+RANK_ID_START));RANK_ID++))
do
    KERNEL_NUM=$(($(nproc)/8))
    PID_START=$((KERNEL_NUM * RANK_ID))
    PID_END=$((PID_START + KERNEL_NUM - 1))
    export CLUSTER_RANK_ID=$((RANK_ID + node_rank * device_number))
    nohup taskset -c $PID_START-$PID_END python3 main.py  \
        --cfg configs/swin_tiny_patch4_window7_224.yaml \
        --data-path ${data_path} \
        --data_shuffle ${data_shuffle} \
        --addr ${master_addr} \
        --port ${master_port} \
        --iter ${iter} \
        --one_epoch \
        --batch-size ${batch_size} \
        --local_rank $RANK_ID > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
done

wait

if [[ x"${master_addr}" == x"${HCCL_IF_IP}" ]];then
    ##################获取训练数据################

    #训练结束时间，不需要修改
    end_time=$(date +%s)
    e2e_time=$(( $end_time - $start_time ))

    #结果打印，不需要修改
    echo "------------------ Final result ------------------"
    #输出性能FPS，需要模型审视修改
    FPS=`grep -a 'FPS'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F " " '{print $3}'|awk 'END {print}'`
    #打印，不需要修改
    echo "Final Performance images/sec : $FPS"

    #输出训练精度,需要模型审视修改
    train_accuracy=`grep -a '* Acc@1'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print}'|awk -F "Acc@1" '{print $NF}'|awk -F " " '{print $1}'`
    #打印，不需要修改
    echo "Final Train Accuracy : ${train_accuracy}"
    echo "E2E Training Duration sec : $e2e_time"

    #性能看护结果汇总
    #训练用例信息，不需要修改
    BatchSize=${batch_size}
    DeviceType=`uname -m`
    CaseName=${Network}_bs${BatchSize}_${WORLD_SIZE}'p'_'acc'

    ##获取性能数据，不需要修改
    #吞吐量
    ActualFPS=${FPS}
    #单迭代训练时长
    TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*1000/'${FPS}'}'`

    #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
    grep Train: ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|grep -v Test|awk -F "loss" '{print $NF}' | awk -F " " '{print $1}' >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

    #最后一个迭代loss值，不需要修改
    ActualLoss=`awk 'END {print}'  ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

    #关键信息打印到${CaseName}.log中，不需要修改
    echo "Network = ${Network}" >  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "RankSize = ${WORLD_SIZE}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "BatchSize = ${BatchSize}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "DeviceType = ${DeviceType}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "CaseName = ${CaseName}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "ActualFPS = ${ActualFPS}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "TrainingTime = ${TrainingTime}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "TrainAccuracy = ${train_accuracy}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "ActualLoss = ${ActualLoss}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "E2ETrainingTime = ${e2e_time}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
fi
